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product-manager-toolkit

Office & productivity

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Updated 10-15 19:56

About this skill

Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.

Quick Start

For Feature Prioritization

python scripts/rice_prioritizer.py sample  # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15

For Interview Analysis

python scripts/customer_interview_analyzer.py interview_transcript.txt

For PRD Creation

  1. Choose template from references/prd_templates.md
  2. Fill in sections based on discovery work
  3. Review with stakeholders
  4. Version control in your PM tool

Core Workflows

Feature Prioritization Process

  1. Gather Feature Requests

    • Customer feedback
    • Sales requests
    • Technical debt
    • Strategic initiatives

  2. Score with RICE

    # Create CSV with: name,reach,impact,confidence,effort
python scripts/rice_prioritizer.py features.csv
    • Reach: Users affected per quarter
    • Impact: massive/high/medium/low/minimal
    • Confidence: high/medium/low
    • Effort: xl/l/m/s/xs (person-months)

  3. Analyze Portfolio

    • Review quick wins vs big bets
    • Check effort distribution
    • Validate against strategy

  4. Generate Roadmap

    • Quarterly capacity planning
    • Dependency mapping
    • Stakeholder alignment

Customer Discovery Process

  1. Conduct Interviews

    • Use semi-structured format
    • Focus on problems, not solutions
    • Record with permission

  2. Analyze Insights

    python scripts/customer_interview_analyzer.py transcript.txt

    Extracts:

    • Pain points with severity
    • Feature requests with priority
    • Jobs to be done
    • Sentiment analysis
    • Key themes and quotes

  3. Synthesize Findings

    • Group similar pain points
    • Identify patterns across interviews
    • Map to opportunity areas

  4. Validate Solutions

    • Create solution hypotheses
    • Test with prototypes
    • Measure actual vs expected behavior

PRD Development Process

  1. Choose Template

    • Standard PRD: Complex features (6-8 weeks)
    • One-Page PRD: Simple features (2-4 weeks)
    • Feature Brief: Exploration phase (1 week)
    • Agile Epic: Sprint-based delivery

  2. Structure Content

    • Problem → Solution → Success Metrics
    • Always include out-of-scope
    • Clear acceptance criteria

  3. Collaborate

    • Engineering for feasibility
    • Design for experience
    • Sales for market validation
    • Support for operational impact

Key Scripts

rice_prioritizer.py

Advanced RICE framework implementation with portfolio analysis.

Features:

  • RICE score calculation
  • Portfolio balance analysis (quick wins vs big bets)
  • Quarterly roadmap generation
  • Team capacity planning
  • Multiple output formats (text/json/csv)

Usage Examples:

# Basic prioritization
python scripts/rice_prioritizer.py features.csv

# With custom team capacity (person-months per quarter)
python scripts/rice_prioritizer.py features.csv --capacity 20

# Output as JSON for integration
python scripts/rice_prioritizer.py features.csv --output json

customer_interview_analyzer.py

NLP-based interview analysis for extracting actionable insights.

Capabilities:

  • Pain point extraction with severity assessment
  • Feature request identification and classification
  • Jobs-to-be-done pattern recognition
  • Sentiment analysis
  • Theme extraction
  • Competitor mentions
  • Key quotes identification

Usage Examples:

# Analyze single interview
python scripts/customer_interview_analyzer.py interview.txt

# Output as JSON for aggregation
python scripts/customer_interview_analyzer.py interview.txt json

Reference Documents

prd_templates.md

Multiple PRD formats for different contexts:

  1. Standard PRD Template

    • Comprehensive 11-section format
    • Best for major features
    • Includes technical specs

  2. One-Page PRD

    • Concise format for quick alignment
    • Focus on problem/solution/metrics
    • Good for smaller features

  3. Agile Epic Template

    • Sprint-based delivery
    • User story mapping
    • Acceptance criteria focus

  4. Feature Brief

    • Lightweight exploration
    • Hypothesis-driven
    • Pre-PRD phase

Prioritization Frameworks

RICE Framework

Score = (Reach × Impact × Confidence) / Effort

Reach: # of users/quarter
Impact: 
  - Massive = 3x
  - High = 2x
  - Medium = 1x
  - Low = 0.5x
  - Minimal = 0.25x
Confidence:
  - High = 100%
  - Medium = 80%
  - Low = 50%
Effort: Person-months

Value vs Effort Matrix

         Low Effort    High Effort
         
High     QUICK WINS    BIG BETS
Value    [Prioritize]   [Strategic]
         
Low      FILL-INS      TIME SINKS
Value    [Maybe]       [Avoid]

MoSCoW Method

  • Must Have: Critical for launch
  • Should Have: Important but not critical
  • Could Have: Nice to have
  • Won't Have: Out of scope

Discovery Frameworks

Customer Interview Guide

1. Context Questions (5 min)
   - Role and responsibilities
   - Current workflow
   - Tools used

2. Problem Exploration (15 min)
   - Pain points
   - Frequency and impact
   - Current workarounds

3. Solution Validation (10 min)
   - Reaction to concepts
   - Value perception
   - Willingness to pay

4. Wrap-up (5 min)
   - Other thoughts
   - Referrals
   - Follow-up permission

Hypothesis Template

We believe that [building this feature]
For [these users]
Will [achieve this outcome]
We'll know we're right when [metric]

Opportunity Solution Tree

Outcome
├── Opportunity 1
│   ├── Solution A
│   └── Solution B
└── Opportunity 2
    ├── Solution C
    └── Solution D

Metrics & Analytics

North Star Metric Framework

  1. Identify Core Value: What's the #1 value to users?
  2. Make it Measurable: Quantifiable and trackable
  3. Ensure It's Actionable: Teams can influence it
  4. Check Leading Indicator: Predicts business success

Funnel Analysis Template

Acquisition → Activation → Retention → Revenue → Referral

Key Metrics:
- Conversion rate at each step
- Drop-off points
- Time between steps
- Cohort variations

Feature Success Metrics

  • Adoption: % of users using feature
  • Frequency: Usage per user per time period
  • Depth: % of feature capability used
  • Retention: Continued usage over time
  • Satisfaction: NPS/CSAT for feature

Best Practices

Writing Great PRDs

  1. Start with the problem, not solution
  2. Include clear success metrics upfront
  3. Explicitly state what's out of scope
  4. Use visuals (wireframes, flows)
  5. Keep technical details in appendix
  6. Version control changes

Effective Prioritization

  1. Mix quick wins with strategic bets
  2. Consider opportunity cost
  3. Account for dependencies
  4. Buffer for unexpected work (20%)
  5. Revisit quarterly
  6. Communicate decisions clearly

Customer Discovery Tips

  1. Ask "why" 5 times
  2. Focus on past behavior, not future intentions
  3. Avoid leading questions
  4. Interview in their environment
  5. Look for emotional reactions
  6. Validate with data

Stakeholder Management

  1. Identify RACI for decisions
  2. Regular async updates
  3. Demo over documentation
  4. Address concerns early
  5. Celebrate wins publicly
  6. Learn from failures openly

Common Pitfalls to Avoid

  1. Solution-First Thinking: Jumping to features before understanding problems
  2. Analysis Paralysis: Over-researching without shipping
  3. Feature Factory: Shipping features without measuring impact
  4. Ignoring Technical Debt: Not allocating time for platform health
  5. Stakeholder Surprise: Not communicating early and often
  6. Metric Theater: Optimizing vanity metrics over real value

Integration Points

This toolkit integrates with:

  • Analytics: Amplitude, Mixpanel, Google Analytics
  • Roadmapping: ProductBoard, Aha!, Roadmunk
  • Design: Figma, Sketch, Miro
  • Development: Jira, Linear, GitHub
  • Research: Dovetail, UserVoice, Pendo
  • Communication: Slack, Notion, Confluence

Quick Commands Cheat Sheet

# Prioritization
python scripts/rice_prioritizer.py features.csv --capacity 15

# Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt

# Create sample data
python scripts/rice_prioritizer.py sample

# JSON outputs for integration
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json

Integration with Agent Collaboration Framework

This toolkit can seamlessly integrate with the Agent Collaboration Framework to enable intelligent collaboration for product teams.

Integration Scenarios

Scenario 1: Feature Prioritization Review Meeting

Combine the RICE ranking tool with multi-agent meeting decision-making:

# 1. 使用RICE脚本生成初步排序
python scripts/rice_prioritizer.py features.csv --capacity 15

# 2. 调用智能体团队进行会议讨论
"请用产品团队评审以下功能的优先级：[功能列表]"

# 3. 输出包含RICE分数和会议共识的完整决策

Participating Agents:

  • Product Manager (facilitator, using the RICE method)
  • Technical Architect (evaluates implementation difficulty)
  • Market Analyst (evaluates market value)
  • Financial Advisor (evaluates ROI)

Related Skills:

  • agent-team: Agent Collaboration Framework
  • multi-agent-meeting: Meeting Decision-Making Process

Meeting Template: multi-agent-meeting/assets/meeting-templates/product-feature-review.md

Scenario 2: Customer Insight Analysis Meeting

Combine the interview analysis tool with an agent team:

# 1. 使用分析脚本提取洞察
python scripts/customer_interview_analyzer.py interview.txt

# 2. 调用智能体团队讨论改进方案
"分析这份访谈记录并生成产品改进方案：[访谈文本]"

# 3. 输出包含洞察、方案和PRD草稿的完整报告

Participating Agents:

  • Product Manager (solution development)
  • User Researcher (insight extraction)
  • Designer (experience improvement)
  • Technical Architect (feasibility assessment)

Related Skills:

  • agent-team: Agent Collaboration Framework
  • multi-agent-meeting: Meeting Decision-Making Process

Meeting Template: multi-agent-meeting/assets/meeting-templates/customer-insight-analysis.md

Scenario 3: Product Roadmap Planning Meeting

Combine strategic analysis and RICE ranking:

# 1. 战略分析智能体识别市场机会
"扫描AI内容生成领域的市场机会"

# 2. 使用RICE方法对机会进行排序
python scripts/rice_prioritizer.py opportunities.csv --capacity 15

# 3. 召开路线图规划会议
"制定Q2产品路线图，团队容量15人月"

# 4. 输出季度路线图和资源分配计划

Participating Agents:

  • Product Manager (roadmap planning)
  • Strategy Analysis Agent (market opportunity identification)
  • Technical Architect (technical feasibility)
  • Project Manager (resource and time assessment)

Related Skills:

  • agent-team: Agent Collaboration Framework (Scenario 9: Product Roadmap Planning Meeting)

Product Manager Agent Definition

When using the "Product Manager Agent" in the Agent Collaboration Framework, this agent has the following capabilities:

Professional Knowledge:

  • RICE prioritization method
  • Customer interview analysis techniques
  • PRD document writing standards
  • Product roadmap planning
  • North Star metric definition

Tool Invocation:

# 优先级排序
self.call_tool("rice_prioritizer", features_csv, capacity=15)

# 访谈分析
self.call_tool("customer_interview_analyzer", interview_text)

# PRD生成
self.use_template("prd_templates", template_type="standard")

Collaboration Interface:

  • Input: Feature list, user feedback, market analysis
  • Output: Priority ranking, PRD document, product roadmap

Applicable Scenarios:

  • Feature review meeting
  • Customer insight analysis
  • Product roadmap planning
  • Requirement document writing

Agent Definition: Reference agent-team/references/agent-registry.md "Product Manager Agent (Enhanced Version)" in

User Researcher Agent Definition

Professional Knowledge:

  • Customer interview analysis
  • Pain point identification and severity assessment
  • User persona construction
  • Jobs-to-be-Done analysis
  • Sentiment analysis and topic extraction

Tool Invocation:

# 访谈分析
self.call_tool("customer_interview_analyzer", interview_text, output_format="json")

# 提取关键洞察
insights = self.extract_insights(analysis_result)

Collaboration Interface:

  • Input: Interview records, user feedback, behavioral data
  • Output: Insight report, pain point list, user persona

Use Cases:

  • Customer interview analysis
  • User needs discovery
  • Product improvement direction identification

Agent Definition: Refer to agent-team/references/agent-registry.md the "User Researcher Agent" in

Complete Collaboration Example

User Input:

我有以下功能需要评审优先级：
1. AI自动生成脚本
2. 多平台发布
3. 数据分析看板
4. 用户协作功能
5. API开放平台

请帮我评审并生成优先级排序，团队容量15人月。

System Execution Flow:

  1. Identify scenario: Product feature prioritization review (agent-team scenario 7)

  2. Assemble team:

    • Product Manager (facilitator)
    • Technical Architect
    • Market Analyst
    • Financial Advisor

  3. Data preparation:

    # 产品经理智能体调用RICE脚本
python scripts/rice_prioritizer.py features.csv --capacity 15

  4. Meeting discussion (using the multi-agent-meeting process):

    > 产品经理: 根据RICE排序，AI自动生成脚本得分最高(85分)...
> 技术架构师: 从技术角度，这个功能可行，可以使用现有大模型...
> 市场分析师: 市场调研显示，用户对AI功能的需求强烈...
> 财务顾问: 预计3个月可以回本，ROI较高...

  5. Decision output:

    # 功能优先级排序结果

## 最终排序
1. AI自动生成脚本 (RICE: 85) - P0
2. 多平台发布 (RICE: 72) - P1
3. API开放平台 (RICE: 58) - P2

## Q2开发计划
- AI自动生成脚本: 3人月
- 多平台发布: 2人月
- 预留缓冲: 1人月

Related Resources

Agent Collaboration Framework:

  • Skill name: agent-team
  • Agent registry: agent-team/references/agent-registry.md
  • Collaboration template: agent-team/references/collaboration-templates.md

Meeting Decision Process:

  • Skill name: multi-agent-meeting
  • Meeting template: multi-agent-meeting/assets/meeting-templates/
  • Meeting record format: multi-agent-meeting/references/meeting-record-format.md

Product Scenario Templates:

  • Scenario 7: Product feature prioritization review meeting
  • Scenario 8: Customer interview insight analysis meeting
  • Scenario 9: Product roadmap planning meeting
  • Scenario 10: Product pricing strategy meeting

Integration Advantages

Before Integration:

  • Tools used independently, requiring manual combination
  • Lack of multi-perspective professional analysis
  • Decision-making process is not transparent

After Integration:

  • ✅ One-click launch of the complete collaboration process
  • ✅ Multi-agent professional analysis
  • ✅ Real-time display of the discussion process
  • ✅ Standardized decision output
  • ✅ Automatic tool invocation

User Value:

  • Save time: from manual combination to one-click launch
  • Improve quality: multi-perspective professional analysis
  • Lower the threshold: preset templates and processes
  • Complete closed loop: from data analysis to decision output